In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of “black-box” models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs.

在最近的一篇论文中，Baño-Medina 等人。（用于统计降尺度的深度学习神经模型的配置和比对。预印本，2019 年）使用大规模“完美”再分析预测器评估了深度卷积神经网络 (CNN) 在降低欧洲温度和降水量方面的适用性。他们将 CNN 提供的结果与从传统上用于降尺度目的（线性和广义线性模型）的一组标准方法中获得的结果进行了比较，得出结论认为 CNN 非常适合整个大陆的应用。通过使用全球气候模型 (GCM) 输出作为预测因子来评估 CNN 对降低未来气候变化预测的适用性，该分析得到了扩展。这与这种类型的“黑盒”模型特别相关，基于物理原因无法轻易解释其结果，并且可能由于不受控制的外推伪影而导致难以置信的缩小投影。基于这个前提，我们在这项工作中分析了在完美的预后降尺度：(1) 为构建统计模型而选择的预测因子应能被 GCM 很好地再现；(2) 统计模型应能够可靠地推断出样本（气候变化）条件。作为测试这些模型适用性的第一步，这里通过分析 CNN 如何影响原始 GCM 气候变化信号（定义为未来和历史气候之间的差异或增量）来评估后一个假设。我们的结果表明，与完善的广义线性模型 (GLM) 相比，CNN 与本世纪末原始 GCM 输出的偏差更小，从而为气候变化应用提供了更合理的降尺度结果。此外，由于空间特征的自动处理，